Abstract
3D visualization of digital human becomes a key tool for the medical visualization, especially for medical education. Web3D technology has been commonly applied in this field. However, the quality of rendering is not expected for the medical purpose. Nowadays, global illumination (GI) map is an efficient tool for real-time lighting and shadow rendering. On the cloud baking server, a large number of rendered GI maps are generated under variety of configuration in the scene on the Web3D interface end. GI tree works on organizing these baked maps for reusing in the Web3D client. Meanwhile, it dispatches the existing baked maps directly in the case that the viewpoint appears in the duplicate positions in the Web3D client. This is the main stream solution of the cloud pre-rendering. However, it is a challenge to store and manage excessive rendered maps. In this paper, we propose a light-weight collaborative machine learning method for lighting and shadow rendering in medical applications. In this system, the conditional generative adversarial networks (GAN) works for generating the GI map instead of finding out the similar from number of stored maps, and we propose structure-aware 3D image warping method to improve the system performance. The experiments demonstrated that our proposed system not only guarantees the resolution of the GI map in the Web3D client, but also significantly reduces the rendering computational needs so as to improve the system performance.
Highlights
Nowadays, 3D visualization of digital human becomes the key tool for the medical visualization and education
The lightweight model data is different from the original data. These differences makes cloud baking system optimize distortion based on the pixel-level 3D image warping method.This paper aims to the medical models, so the scene data is not very huge, and there is unnecessary to use lightweight models at the Web3D client
We build on Tensorflow.js framework to predict global illumination (GI) maps using generative adversarial networks (GAN) on the Web3D client
Summary
3D visualization of digital human becomes the key tool for the medical visualization and education. B. GENERATIVE ADVERSARIAL NETWORKS The real-time global illumination rendering is practically a mapping from screen space buffer, for instance, the position map, normal map, reflection map, and other attributes in a virtual 3D scene, to various frame images of screen effect. GENERATIVE ADVERSARIAL NETWORKS The real-time global illumination rendering is practically a mapping from screen space buffer, for instance, the position map, normal map, reflection map, and other attributes in a virtual 3D scene, to various frame images of screen effect This is the typical task of image-to-image translation.
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